Fire Detection using Geostationary and Polar

Orbiting Satellites

Dr. Bernadette ConnellCIRA/CSU/RAMMT

Dr. Vilma CastroUCR/RMTC

March 2005

Objectives

• Background

• Environmental and weather conditions conducive to fires

• Satellite fire detection techniques for hot spots

• Examples

• Lab exercise

Monitoring Fire ActivityWhy?• To detect and monitor wildfires in real-time for

response and mitigation.– Are the fires posing danger to population centers

or economic resources?

• To determine trends in fire activity from year to year. – Are they the result of agriculture burning and

deforestation? – Are they the result of a buildup of fuels? – Are they affected by drought?

• To determine the extent of smoke transport• To determine the effect of burning on the

environment.

United States - Fire Weather Activities

• Various FIRE DANGER RATING systems have been developed to express fire hazard.

They incorporate some of these basic questions:• Are the “fuels” dry enough to burn?• Is the current or forecast weather conducive to

starting fires and sustaining them?– Is it dry, windy?– Is the atmosphere stable or unstable? – Will there be lightning with very little rain?

United States - Fire Weather Activities

• To address the condition of fuels:– Long term monitoring for drought (satellite)– Monitoring of vegetation health and accumulation of dead

vegetation (fuels) (satellite and ground)

• To address weather conditions:– Outlooks for precipitation and temperature

(climatology/model prognosis)

• Information Sources:– Climate Prediction Center (CPC)– USDA Forest Service– NOAA/NESDIS/ORA

Real-time NWS Fire Weather Services

• Storm Prediction Center – issues 1 and 2 day fire outlookshttp://www.spc.noaa.gov/products/fire_wx – maps– text discussion– hazard categores:

• critical areas – outlines• extremely critical – hatched• dry thunderstorm risk - scalloped

Real-time NWS Fire Weather Services

• Weather Forecast Offices – issues fire weather forecasts/watches, smoke forecasts, red flag warnings, spot forecasts

• IMET – Incident METeorological information for fire behavior forecasts, spot forecasts, nowcasts

Real-time (non-routine) Products

• Fire Weather Watch; valid 24-48 hr– 1-min sustained winds at 20 ft. > 15-25 kts– Relative humidity < threshold (see following slide –

varies by region)– Temperature >65-75°F– Vegetation moisture <8-12%

• Red Flag Warning: valid 0-24 hr– Same criteria as Fire Weather Watch (above)

• “Spot” Forecasts– Forecasts for prescribed burns, rescues, wildfires in

progress

Threshold Relative Humidities for Red Flag Watches/Warnings

Haines Index

• This index is correlated with fire growth in plume dominated fires

• Composed of two parts:– stability: temperature difference between two atmospheric layers

near the surface– moisture: temperature/dew point difference for that layer

• The index is adaptable for varying elevation regimes• Index value estimates rate of spread:

2-3: Very Low Potential (Moist Stable Lower Atmosphere)4: Low Potential5: Moderate Potential6: High Potential ( Dry Unstable Lower Atmosphere)

Calculating Haines Index

LOW ELEVATION <2,000 FT

Stability Term (T950-T850)

1… 3 C or less

2… 4 to 7 C

3… >= 8 C

Moisture Term (T850-Td 850)

1… 5 C or less

2… 6 to 9 C

3… >= 10 C

MID ELEVATION

2,000-6,000 FT

Stability Term (T850-T700)

1… 5 C or less

2… 6 to 10 C

3… >= 11 C

Moisture Term (T850-Td 850)

1… 5 C or less

2… 6 to 12 C

3… >= 13 C

HIGH ELEVATION

>6,000 FT

Stability Term (T700-T500)

1… 17 C or less

2… 18 to 21 C

3… >= 22 C

Moisture Term (T700-Td 700)

1… 14 C or less

2… 15 to 20 C

3… >= 21 C

Sum of two terms = Haines IndexGOES Fire Detection - VISITview

2-very low3-very low4-low

5-moderate6-highwater

U.S. Drought Monitor – Severity Classification

Category Description Fire RiskPalmer Drought Index

CPC Soil Moisure (percentiles)

Weekly Streamflow (percentiles)

% of

Normal Precip

Standardized Precipitation Index

Satellite Vegetation Health Index

D0Abnormally Dry

Above average

-1.0 to

-1.921-30 21-30

<75%

for 3

months

-0.5 to -0.7 36-45

D1Moderate Drought

High-2.0 to

-2.911-20 11-20

<70%

for 3

months

-0.8 to -1.2 26-35

D2Severe Drought

Very high-3.0 to

-3.96-10 6-10

<65%

for 6

months

-1.3 to -1.5 16-25

D3Extreme Drought

Extreme-4.0 to

-4.93.5 3-5

<60%

for 6

months

-1.6 to -1.9 6-15

D4Exceptional Drought

Exceptional and Widespread

< -5.0 0-2 0-2<65%

for 12 months

< -2.0 1-5

GOES Fire Detection - VISITview

Vegetation Health

• Showing vegetation health for this year compared with last year.• Fire becomes a concern when the vegetation is stressed (values less than 50) and when drought and other weather is of concern.

Loop of plume dominated fire

VIS 03246

IR2 03246

WashingtonOregon

Idaho

Montana

British Columbia Alberta

Loop of wind driven fire

VIS

Mexico

California

IR2

IR2 24hr

Satellite Monitoring of FIRESGeostationary or Polar Orbiting?

• Monitoring from both types of satellites utilize visible, shortwave, and longwave infrared channel observations.

• Geostationary Satellites (GOES)– Coarser resolution (~4km)– Good temporal resolution (every 15-30 min.) which

provides information on the diurnal timing and spatial distribution of fires.

– Saturation brightness temperature: 338K (for GOES-8 and 12)

• Polar Orbiting Satellites (AVHRR)– Finer resolution (~1km)– Only 2 passes per day– Saturation brightness temperature: 320 K

“Quick” RAMSDIS Products for fire detection

These products are made with images from channels

3.9 and 10.7 µm

NIGHT: Fog-Stratus ProductDAY: Reflectivity Product

Characteristics of 3.9 micrometer channel that make it suitable for “hot” spot detection

Radiance is not linear with temperature

• A small change in radiance at 300 K at 3.9 um creates a larger change in temperature than at 10.7 um(note the different scales: 3.9 um from 0-410.7 um from 0-200

180 220 260 300 340Temperature (K)

0

1

2

3

4

Rad

ianc

e (m

W/(m

2.sr

.cm

-1)

180 220 260 300 340Temperature (K)

0

50

100

150

200

Rad

ianc

e (m

W/(m

2.sr

.cm

-1) wavelength = 10.7 um

wavelength = 3.9 um

Sub pixel response• Rλ = Rλ cloud * % area cloud + Rλ ground * % area ground

• Similarly for fires:

Rλ = Rλ fire * % area fire + Rλ ground * % area ground

GO

ES

3.9 um C

hannel Tutorial

NIGHT: Fog-Stratus Product

Subtract temperature, pixel by pixel, of: 10.7m - 3.9 m images

The result is a negative number

As temperature is warmer at 3.9 m

NIGHT: Fog-Stratus Product

• The result is normalized by adding 150 to each pixel’s value

• Values correspond to a scale of 0.1 K per brightness unit

In a black and white color table, pixels with fire appear darker than the background

NIGHT: Fog-Stratus Product

Pixels with fire are 80 brightness units darker than the background

Observations:

1 brightness unit = 0.1 Kelvin

80 brightness units = 8 K

Temperature difference Temperature difference among pixels without fire: among pixels without fire: 3 K

4- 6 K Difference among pixels: 4- 6 K Difference among pixels: fire cannot be detected with certaintyfire cannot be detected with certainty

DAY: Reflectivity Product

• Channels involved: 3.9 and 10.7 microns• Reflective component is subtracted from the 3.9

micron signal. The temperature at 10.7 microns is used to

estimate the reflective component at 3.9 microns• Fires appear as white spots• Do not need to set thresholds• Limited to daytime use

Reflectivity Product

Observations

• Products allow the identification of fires smaller than a pixel

• Weaver et al. show that it is possible to detect:– 500K fires against a 300K background – covering only 5 % of a 4 x 4 km pixel

Weaver, J.F., Purdom, J.F.W, and Schneider, T.L. 1995. Observing forest fires with the GOES-8, 3.9 µm imaging channel. Weather and Forecasting, 10, 803-808

Observations

• Can the visible channel be used to detect fire?

Yes. The smoke plume can be seen in the visible.

However: Fire must be well developed to create a plume that can be detected in the visible.

Types of Fire Detection Algorithms

• Fixed threshold techniques– Rely on pre-set thresholds and consider a single pixel

at a time.

• Spatial analysis or contextual techniques– Compute relative thresholds based on statistics

calculated from neighboring pixels.

Real-time products for Central America:

http://www.cira.colostate.edu/ramm/sica/main.html

Example of Fixed Threshold Algorithm by Arino et al. (1993)

1. BT3.9 > 320 K (to identify probable fires)

2. BT3.9 – BT10.7 > 15 K

3. BT10.7 > 245 K (to prevent false alarms due to reflective clouds)

GOES-8 3.9 micronGOES-8 3.9 micrometer

GOES-8 3.9 micrometer

Blue areas represent pixels:T3.9 >320K

GOES-8 Product: T3.9 – T10.7

Blue regions represent pixels with:T3.9 – T10.7 > 15 K

Resulting Fire Threshold ProductBlue represents fire pixels

Problems

• Very warm, dry ground is detected as fire.

• Will not pick up night fires that are cooler than 320 K

Example of Contextual Algorithm by Justice et al. (1996)

1. BT3.9 > 316 K (to identify probable fires)2. Estimate a background temperature with

surrounding ‘valid’ pixels:A valid pixel * Is not a cloud

* Is not a fire pixel3. The window starts as a 3X3 pixel area and expands

to a 21X21 pixel grid until at least 25% of the background pixels (or at least 3) are valid.

4. Let DT=MAX(2 std dev of BT3.9-BT10.7, 5 K)FIRE pixel:

if BT3.9-BT10.7 > mean BT3.9-BT10.7 + DT

and BT10.7 > mean BT10.7 – std dev of BT10.7

Fire Justice ProductBlue pixels represent detected fires

Problems

• Does not adequately detect fire pixels in regions of very warm and dry ground.

• May also need to implement a correction for (horizontal) temperature changes in mountainous regions.

• Will not pick up night fires that are cooler than 316 K

GOES- 8 Reflectivity Product

Shot-noise filter applied to Reflectivity ProductRed pixels denote potential fires.

Experimental ABBA

• Automated Biomass Burning Algorithm• Contextual Algorithm• Developed at the Cooperative Institute for

Meteorological Satellite Studies (CIMSS) at the University of Wisconsin in Madison.

• Initially ‘calibrated’ to Brazil Fires

http://cimss.ssec.wisc.edu/goes/burn/wfabba.html

Polar Orbiting Satellites

• The same detection algorithms presented here can be applied to imagery from polar orbiting satellites.

• For AVHRR, the 3.9 um sensor saturates at ~ 323 K

(GOES-8 saturates at 338 K)

• We will view an example of GOES vs. AVHRR imagery in the lab.

References/links

GOES Fire Detection – VISITview sessionhttp://www.cira.colostate.edu/ramm/visit/detection.html

see reference/links at the bottom of their page

Fire Products for Central Americahttp://www.cira.colostate.edu/ramm/sica/main.html

Wildfire ABBAhttp://cimss.ssec.wisc.edu/goes/burn/wfabba.html

CIRA GOES 3.9 um Channel Tutorialhttp://www.cira.colostate.edu/ramm/goes39/cover.htm

Storm Prediction Center – 1 and 2 day fire outlookshttp://www.spc.noaa.gov/products/fire_wx

Drought Monitor - long term drought indicators for the US:Drought Index, Crop Moisture Index, Standardized Precipitation Index, Percent of

Normal Rainfall, Daily Streamflow, Snowpack, Soil Moisture, Vegetation Health

http://drought.unl.edu/dm